A Pathfinding Algorithm for Lowering Infection Exposure of Healthcare Personnel Working in Makeshift Hospitals.

Area of Science:

• Computer Science
• Healthcare Management
• Epidemiology

Background:

• Makeshift hospitals became critical during the COVID-19 pandemic.
• Healthcare personnel (HCP) require safe and efficient navigation in these environments.
• Minimizing disease exposure for HCP is paramount in airborne illness settings.

Purpose of the Study:

• To develop and verify pathfinding algorithms for HCP in makeshift hospitals.
• To assess algorithms' effectiveness in reducing disease exposure and travel distance.
• To investigate the impact of environmental factors (grid size, unit shape, movement) on pathfinding.

Main Methods:

• Developed novel pathfinding algorithms, including quantum-based and mixed path approaches.
• Utilized a 2D grid model to simulate makeshift hospital layouts.
• Measured total distance traveled and total disease exposure for evaluated paths.

Main Results:

• Quantum-based algorithms reduced disease exposure by 50-66% compared to shortest path, with a 33-50% increase in distance.
• Mixed path algorithms reduced disease exposure by 33-50%, with only a 10-20% increase in distance.
• Algorithm performance was influenced by grid unit size, shape, and movement parameters.

Conclusions:

• Pathfinding algorithms can significantly enhance HCP safety in makeshift hospitals.
• Quantum-based and mixed path algorithms offer effective strategies for minimizing disease risk.
• These algorithms provide a valuable tool for managing healthcare worker well-being during pandemics.